z-logo
open-access-imgOpen Access
A New Class of Efficient and Robust Energy Stable Schemes for Gradient Flows
Author(s) -
Jie Shen,
Jie Xu,
Jiang Yang
Publication year - 2019
Publication title -
siam review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.683
H-Index - 120
eISSN - 1095-7200
pISSN - 0036-1445
DOI - 10.1137/17m1150153
Subject(s) - nonlinear system , construct (python library) , mathematics , scalar (mathematics) , energy (signal processing) , computer science , class (philosophy) , mathematical optimization , constant (computer programming) , geometry , artificial intelligence , physics , statistics , quantum mechanics , programming language
We propose a new numerical technique to deal with nonlinear terms in gradient flows. By introducing a scalar auxiliary variable (SAV), we construct efficient and robust energy stable schemes for a large class of gradient flows. The SAV approach is not restricted to specific forms of the nonlinear part of the free energy, and only requires to solve {\it decoupled} linear equations with {\it constant coefficients}. We use this technique to deal with several challenging applications which can not be easily handled by existing approaches, and present convincing numerical results to show that our schemes are not only much more efficient and easy to implement, but can also better capture the physical properties in these models. Based on this SAV approach, we can construct unconditionally second-order energy stable schemes; and we can easily construct even third or fourth order BDF schemes, although not unconditionally stable, which are very robust in practice. In particular, when coupled with an adaptive time stepping strategy, the SAV approach can be extremely efficient and accurate.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom